{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from bertopic import BERTopic\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from transformers.pipelines import pipeline\n",
        "from umap import UMAP\n",
        "from hdbscan import HDBSCAN\n",
        "from sklearn.feature_extraction.text import CountVectorizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 加载数据"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "条数:  1000\n",
            "预览第一条:  文旅文 创看 洛阳 河南省 文旅文创 发展 大会 本次 大会 安排 项目 签约 主要 方面 内容 一是 文旅 产业 项目 签约 截至 目前 梳理 重点 文旅 项目 投资总额 525.6 亿元 遴选 重大项目 进行 现场 签约 投资总额 365.8 亿元 项目 包括 文物 数字化 开发 文化 创意 园区 建设 文化 项目 涵盖 旅游 度假区 建设 旅游 酒店 民宿 打造 旅游 项目 既有 旅游 景区 开发 商旅 综合体 建设 传统 业态 项目 宇宙 基地 沉浸 演艺 业态 项目 充分体现 我省 文化 旅游 发展 特点 趋势 二是 引客 入豫 项目 签约 主要 我省 文旅 部门 文旅 企业 头部 旅行 知名 OTA 平台 重点 客源地 文旅 部门 签订 引客 入豫 协议 持续 拓展 省外 客源 市场\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# step1 加载文件\n",
        "with open('../../data/切词.txt', 'r', encoding='utf-8') as file:\n",
        "  docs = file.readlines()\n",
        "print('条数: ', len(docs))\n",
        "print('预览第一条: ', docs[0])\n",
        "\n",
        "vectorizer_model = None"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 使用BERT-Base-Chinese"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-chinese were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1000, 768)\n"
          ]
        },
        {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Topic</th>\n",
              "      <th>Count</th>\n",
              "      <th>Name</th>\n",
              "      <th>Representation</th>\n",
              "      <th>Representative_Docs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>-1</td>\n",
              "      <td>501</td>\n",
              "      <td>-1_景区_河南_游客_城市</td>\n",
              "      <td>[景区, 河南, 游客, 城市, 洛阳市, 中国, 历史, 活动, 河南省, 发展]</td>\n",
              "      <td>[河南 多家 景区 陆续 发布 开园 公告 台风 杜苏芮 强度 逐渐 减弱 河南 景区 陆续...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>216</td>\n",
              "      <td>0_中国_石窟_龙门石窟_世界</td>\n",
              "      <td>[中国, 石窟, 龙门石窟, 世界, 艺术, 旅行, 景区, 位于, 河南, 历史]</td>\n",
              "      <td>[龙门石窟 中国 石刻 艺术 宝库 现为 世界 文化遗产 全国 重点 文物保护 单位 国家 ...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1_活动_景区_免费_高速</td>\n",
              "      <td>[活动, 景区, 免费, 高速, 门票, 时间, 白云山, 地点, 期间, 体验]</td>\n",
              "      <td>[洛阳 身边 自驾游 栾川 高速 免费 答疑 自驾游 栾川 高速 费全免 问题 需要 了解 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>99</td>\n",
              "      <td>2_发展_城市_项目_河南</td>\n",
              "      <td>[发展, 城市, 项目, 河南, 文旅, 活动, 中国, 国家, 游客, 建设]</td>\n",
              "      <td>[行走 河南 读懂 中国 关注 全省 文旅文创 发展 大会 二十大 报告 指出 坚持 以文塑...</td>\n",
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              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>42</td>\n",
              "      <td>3_地方_很多_一天_时间</td>\n",
              "      <td>[地方, 很多, 一天, 时间, 已经, 一点, 古城, 酒店, 西安, 感受]</td>\n",
              "      <td>[此次 四天 旅行 已经 结束 以往 不用 这次 大学 同学 一起 毕业 一年 小聚 组团 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>42</td>\n",
              "      <td>4_旅客_郑州_高速_景区</td>\n",
              "      <td>[旅客, 郑州, 高速, 景区, 客流, 出行, 发送, 通行, 假期, 高峰]</td>\n",
              "      <td>[国庆 长假 我市 迎来 客流 高峰 公路 铁路 发送 旅客 超过 万人次 今年 国庆 长假...</td>\n",
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              "   Topic  Count             Name                              Representation  \\\n",
              "0     -1    501   -1_景区_河南_游客_城市  [景区, 河南, 游客, 城市, 洛阳市, 中国, 历史, 活动, 河南省, 发展]   \n",
              "1      0    216  0_中国_石窟_龙门石窟_世界  [中国, 石窟, 龙门石窟, 世界, 艺术, 旅行, 景区, 位于, 河南, 历史]   \n",
              "2      1    100    1_活动_景区_免费_高速   [活动, 景区, 免费, 高速, 门票, 时间, 白云山, 地点, 期间, 体验]   \n",
              "3      2     99    2_发展_城市_项目_河南    [发展, 城市, 项目, 河南, 文旅, 活动, 中国, 国家, 游客, 建设]   \n",
              "4      3     42    3_地方_很多_一天_时间    [地方, 很多, 一天, 时间, 已经, 一点, 古城, 酒店, 西安, 感受]   \n",
              "5      4     42    4_旅客_郑州_高速_景区    [旅客, 郑州, 高速, 景区, 客流, 出行, 发送, 通行, 假期, 高峰]   \n",
              "\n",
              "                                 Representative_Docs  \n",
              "0  [河南 多家 景区 陆续 发布 开园 公告 台风 杜苏芮 强度 逐渐 减弱 河南 景区 陆续...  \n",
              "1  [龙门石窟 中国 石刻 艺术 宝库 现为 世界 文化遗产 全国 重点 文物保护 单位 国家 ...  \n",
              "2  [洛阳 身边 自驾游 栾川 高速 免费 答疑 自驾游 栾川 高速 费全免 问题 需要 了解 ...  \n",
              "3  [行走 河南 读懂 中国 关注 全省 文旅文创 发展 大会 二十大 报告 指出 坚持 以文塑...  \n",
              "4  [此次 四天 旅行 已经 结束 以往 不用 这次 大学 同学 一起 毕业 一年 小聚 组团 ...  \n",
              "5  [国庆 长假 我市 迎来 客流 高峰 公路 铁路 发送 旅客 超过 万人次 今年 国庆 长假...  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 1. 词向量模型，同时加载本地训练好的词向量\n",
        "embedding_model = pipeline(\"feature-extraction\", model=\"bert-base-chinese\") # 使用bert-base-chinese\n",
        "embeddings = np.load('../../data/embedding_bbc.npy') # 使用bert-base-chinese向量\n",
        "print(embeddings.shape)\n",
        "\n",
        "# 2. 创建分词模型\n",
        "vectorizer_model = CountVectorizer() # 因为我们已经分好词了，所以这里不需要传入分词函数了\n",
        "\n",
        "# 3. 创建UMAP降维模型\n",
        "umap_model = UMAP(\n",
        "  n_neighbors=15,\n",
        "  n_components=5,\n",
        "  min_dist=0.0,\n",
        "  metric='cosine',\n",
        "  random_state=42  # ⚠️ 防止随机 https://maartengr.github.io/BERTopic/faq.html\n",
        ")\n",
        "\n",
        "# 4. 创建HDBSCAN聚类模型\n",
        "# 如果要建设离群值，可以减小下面两个参数\n",
        "# https://hdbscan.readthedocs.io/en/latest/faq.html\n",
        "hdbscan_model = HDBSCAN(\n",
        "  min_cluster_size=40,\n",
        "  min_samples=40,\n",
        ")\n",
        "\n",
        "# 5. 创建CountVectorizer模型\n",
        "vectorizer_model = CountVectorizer(stop_words=['洛阳', '旅游', '文化'])\n",
        "\n",
        "# 正式创建BERTopic模型\n",
        "topic_model = BERTopic(\n",
        "  embedding_model=embedding_model,\n",
        "  vectorizer_model=vectorizer_model,\n",
        "  umap_model=umap_model,\n",
        "  hdbscan_model=hdbscan_model,\n",
        ")\n",
        "\n",
        "# 查看主题\n",
        "topics, probs = topic_model.fit_transform(docs, embeddings=embeddings) #传入训练好的词向量\n",
        "topic_model.get_topic_info()"
      ]
    },
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                "title": {
                  "font": {
                    "color": "Black",
                    "size": 22
                  },
                  "text": "<b>Documents and Topics</b>",
                  "x": 0.5,
                  "xanchor": "center",
                  "yanchor": "top"
                },
                "width": 1200,
                "xaxis": {
                  "visible": false
                },
                "yaxis": {
                  "visible": false
                }
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# UMAP可视化\n",
        "reduced_embeddings = UMAP(n_neighbors=10, n_components=2, metric='cosine', min_dist=0.0).fit_transform(embeddings)\n",
        "topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings, hide_document_hover=True, hide_annotations=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 使用哈工大模型"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at hfl/chinese-bert-wwm were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1000, 768)\n"
          ]
        },
        {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Topic</th>\n",
              "      <th>Count</th>\n",
              "      <th>Name</th>\n",
              "      <th>Representation</th>\n",
              "      <th>Representative_Docs</th>\n",
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              "      <th>0</th>\n",
              "      <td>-1</td>\n",
              "      <td>353</td>\n",
              "      <td>-1_景区_河南_中国_城市</td>\n",
              "      <td>[景区, 河南, 中国, 城市, 年票, 洛阳市, 历史, 遗址, 游客, 郑州]</td>\n",
              "      <td>[河南 多家 景区 陆续 发布 开园 公告 台风 杜苏芮 强度 逐渐 减弱 河南 景区 陆续...</td>\n",
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              "      <td>0</td>\n",
              "      <td>248</td>\n",
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              "      <td>[景区, 发展, 建设, 项目, 活动, 郑州, 洛阳市, 国家, 工作, 博物馆]</td>\n",
              "      <td>[好消息 洛阳 再添 4A 景区 洛阳市 文化 旅游局 获悉 洛阳 再添 国家 4A 景区 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>111</td>\n",
              "      <td>1_河南_中国_体验_感受</td>\n",
              "      <td>[河南, 中国, 体验, 感受, 地方, 旅行, 历史, 古城, 打卡, 西安]</td>\n",
              "      <td>[洛阳 值得 一去 地方 简单 下来 洛阳 旅游 攻略 建议 夏天 洛阳 洛阳 市区 主要 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>99</td>\n",
              "      <td>2_石窟_龙门石窟_中国_艺术</td>\n",
              "      <td>[石窟, 龙门石窟, 中国, 艺术, 世界, 莫高窟, 位于, 文化遗产, 造像, 朝代]</td>\n",
              "      <td>[旅行 洛阳 龙门石窟 中国 石刻 艺术 宝库 现为 世界 文化遗产 全国 重点 文物保护 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>68</td>\n",
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              "      <td>[活动, 景区, 免费, 高速, 门票, 时间, 白云山, 期间, 栾川, 游客]</td>\n",
              "      <td>[洛阳 身边 自驾游 栾川 高速 免费 答疑 自驾游 栾川 高速 费全免 问题 需要 了解 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>63</td>\n",
              "      <td>4_游客_景区_城市_五一</td>\n",
              "      <td>[游客, 景区, 城市, 五一, 热门, 数据, 显示, 增长, 上海, 假期]</td>\n",
              "      <td>[清明 机票价格 回归 至疫前 水平 清明 出游 人数 三成 面对 越来越近 清明 长假 旅...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>58</td>\n",
              "      <td>5_王府_竹海_旅行_景区</td>\n",
              "      <td>[王府, 竹海, 旅行, 景区, 泉州, 洛阳桥, 仙门, 旅游区, 夏天, 度假]</td>\n",
              "      <td>[王府 竹海 总结 这份 攻略 赶紧 收藏 每次 惊喜 领略 王府 竹海 四季 春夏秋冬 这...</td>\n",
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              "   Topic  Count             Name  \\\n",
              "0     -1    353   -1_景区_河南_中国_城市   \n",
              "1      0    248    0_景区_发展_建设_项目   \n",
              "2      1    111    1_河南_中国_体验_感受   \n",
              "3      2     99  2_石窟_龙门石窟_中国_艺术   \n",
              "4      3     68    3_活动_景区_免费_高速   \n",
              "5      4     63    4_游客_景区_城市_五一   \n",
              "6      5     58    5_王府_竹海_旅行_景区   \n",
              "\n",
              "                                  Representation  \\\n",
              "0      [景区, 河南, 中国, 城市, 年票, 洛阳市, 历史, 遗址, 游客, 郑州]   \n",
              "1     [景区, 发展, 建设, 项目, 活动, 郑州, 洛阳市, 国家, 工作, 博物馆]   \n",
              "2       [河南, 中国, 体验, 感受, 地方, 旅行, 历史, 古城, 打卡, 西安]   \n",
              "3  [石窟, 龙门石窟, 中国, 艺术, 世界, 莫高窟, 位于, 文化遗产, 造像, 朝代]   \n",
              "4      [活动, 景区, 免费, 高速, 门票, 时间, 白云山, 期间, 栾川, 游客]   \n",
              "5       [游客, 景区, 城市, 五一, 热门, 数据, 显示, 增长, 上海, 假期]   \n",
              "6     [王府, 竹海, 旅行, 景区, 泉州, 洛阳桥, 仙门, 旅游区, 夏天, 度假]   \n",
              "\n",
              "                                 Representative_Docs  \n",
              "0  [河南 多家 景区 陆续 发布 开园 公告 台风 杜苏芮 强度 逐渐 减弱 河南 景区 陆续...  \n",
              "1  [好消息 洛阳 再添 4A 景区 洛阳市 文化 旅游局 获悉 洛阳 再添 国家 4A 景区 ...  \n",
              "2  [洛阳 值得 一去 地方 简单 下来 洛阳 旅游 攻略 建议 夏天 洛阳 洛阳 市区 主要 ...  \n",
              "3  [旅行 洛阳 龙门石窟 中国 石刻 艺术 宝库 现为 世界 文化遗产 全国 重点 文物保护 ...  \n",
              "4  [洛阳 身边 自驾游 栾川 高速 免费 答疑 自驾游 栾川 高速 费全免 问题 需要 了解 ...  \n",
              "5  [清明 机票价格 回归 至疫前 水平 清明 出游 人数 三成 面对 越来越近 清明 长假 旅...  \n",
              "6  [王府 竹海 总结 这份 攻略 赶紧 收藏 每次 惊喜 领略 王府 竹海 四季 春夏秋冬 这...  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 1. 词向量模型，同时加载本地训练好的词向量\n",
        "embedding_model = pipeline(\"feature-extraction\", model=\"hfl/chinese-bert-wwm\") # 使用哈工大模型\n",
        "embeddings = np.load('../../data/embedding_hfl.npy') # 使用哈工大向量\n",
        "print(embeddings.shape)\n",
        "\n",
        "# 2. 创建分词模型\n",
        "vectorizer_model = CountVectorizer() # 因为我们已经分好词了，所以这里不需要传入分词函数了\n",
        "\n",
        "# 3. 创建UMAP降维模型\n",
        "umap_model = UMAP(\n",
        "  n_neighbors=15,\n",
        "  n_components=5,\n",
        "  min_dist=0.0,\n",
        "  metric='cosine',\n",
        "  random_state=42  # ⚠️ 防止随机 https://maartengr.github.io/BERTopic/faq.html\n",
        ")\n",
        "\n",
        "# 4. 创建HDBSCAN聚类模型\n",
        "# 如果要建设离群值，可以减小下面两个参数\n",
        "# https://hdbscan.readthedocs.io/en/latest/faq.html\n",
        "hdbscan_model = HDBSCAN(\n",
        "  min_cluster_size=40,\n",
        "  min_samples=10,\n",
        ")\n",
        "\n",
        "# 5. 创建CountVectorizer模型\n",
        "vectorizer_model = CountVectorizer(stop_words=['洛阳', '旅游', '文化'])\n",
        "\n",
        "# 正式创建BERTopic模型\n",
        "topic_model = BERTopic(\n",
        "  embedding_model=embedding_model,\n",
        "  vectorizer_model=vectorizer_model,\n",
        "  umap_model=umap_model,\n",
        "  hdbscan_model=hdbscan_model,\n",
        ")\n",
        "\n",
        "# 查看主题\n",
        "topics, probs = topic_model.fit_transform(docs, embeddings=embeddings) #传入训练好的词向量\n",
        "topic_model.get_topic_info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
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                        "zerolinecolor": "rgb(36,36,36)"
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                      "yaxis": {
                        "backgroundcolor": "white",
                        "gridcolor": "rgb(232,232,232)",
                        "gridwidth": 2,
                        "linecolor": "rgb(36,36,36)",
                        "showbackground": true,
                        "showgrid": false,
                        "showline": true,
                        "ticks": "outside",
                        "zeroline": false,
                        "zerolinecolor": "rgb(36,36,36)"
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                      "zaxis": {
                        "backgroundcolor": "white",
                        "gridcolor": "rgb(232,232,232)",
                        "gridwidth": 2,
                        "linecolor": "rgb(36,36,36)",
                        "showbackground": true,
                        "showgrid": false,
                        "showline": true,
                        "ticks": "outside",
                        "zeroline": false,
                        "zerolinecolor": "rgb(36,36,36)"
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                    "shapedefaults": {
                      "fillcolor": "black",
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                    "ternary": {
                      "aaxis": {
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                        "showgrid": false,
                        "showline": true,
                        "ticks": "outside"
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                      "baxis": {
                        "gridcolor": "rgb(232,232,232)",
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                        "showgrid": false,
                        "showline": true,
                        "ticks": "outside"
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                      "bgcolor": "white",
                      "caxis": {
                        "gridcolor": "rgb(232,232,232)",
                        "linecolor": "rgb(36,36,36)",
                        "showgrid": false,
                        "showline": true,
                        "ticks": "outside"
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                    },
                    "title": {
                      "x": 0.05
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                    "xaxis": {
                      "automargin": true,
                      "gridcolor": "rgb(232,232,232)",
                      "linecolor": "rgb(36,36,36)",
                      "showgrid": false,
                      "showline": true,
                      "ticks": "outside",
                      "title": {
                        "standoff": 15
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                      "zeroline": false,
                      "zerolinecolor": "rgb(36,36,36)"
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                    "yaxis": {
                      "automargin": true,
                      "gridcolor": "rgb(232,232,232)",
                      "linecolor": "rgb(36,36,36)",
                      "showgrid": false,
                      "showline": true,
                      "ticks": "outside",
                      "title": {
                        "standoff": 15
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                      "zeroline": false,
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                },
                "title": {
                  "font": {
                    "color": "Black",
                    "size": 22
                  },
                  "text": "<b>Documents and Topics</b>",
                  "x": 0.5,
                  "xanchor": "center",
                  "yanchor": "top"
                },
                "width": 1200,
                "xaxis": {
                  "visible": false
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                "yaxis": {
                  "visible": false
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          },
          "metadata": {},
          "output_type": "display_data"
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      ],
      "source": [
        "# UMAP可视化\n",
        "reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine',).fit_transform(embeddings)\n",
        "topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings, hide_document_hover=True, hide_annotations=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 使用sentence-transformers模型"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1000, 384)\n"
          ]
        },
        {
          "data": {
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Topic</th>\n",
              "      <th>Count</th>\n",
              "      <th>Name</th>\n",
              "      <th>Representation</th>\n",
              "      <th>Representative_Docs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1</td>\n",
              "      <td>427</td>\n",
              "      <td>-1_景区_城市_河南_游客</td>\n",
              "      <td>[景区, 城市, 河南, 游客, 洛阳市, 中国, 年票, 国家, 发展, 博物馆]</td>\n",
              "      <td>[小洛微 提醒 河南 多家 景区 陆续 发布 开园 公告 台风 杜苏芮 强度 逐渐 减弱 河...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>194</td>\n",
              "      <td>0_中国_石窟_龙门石窟_艺术</td>\n",
              "      <td>[中国, 石窟, 龙门石窟, 艺术, 河南, 世界, 历史, 博物馆, 文化遗产, 位于]</td>\n",
              "      <td>[河南 旅游 之焦 洛阳 --- 龙门石窟 中国 石刻 艺术 宝库 现为 世界 文化遗产 全...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>1_河南_活动_发展_中国</td>\n",
              "      <td>[河南, 活动, 发展, 中国, 洛阳市, 白云山, 主题, 服务, 历史, 工作]</td>\n",
              "      <td>[行走 河南 读懂 中国 关注 全省 文旅文创 发展 大会 二十大 报告 指出 坚持 以文塑...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>132</td>\n",
              "      <td>2_景区_游客_高速_旅客</td>\n",
              "      <td>[景区, 游客, 高速, 旅客, 郑州, 门票, 活动, 免费, 假期, 高铁]</td>\n",
              "      <td>[日起 河南 景区 门票 河南省政府 新闻办 召开 新闻 发布会 获悉 豫见 春天 惠游 老...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>3_景区_王府_竹海_旅行</td>\n",
              "      <td>[景区, 王府, 竹海, 旅行, 游客, 位于, 小浪底, 黄河, 仙门, 拍照]</td>\n",
              "      <td>[王府 竹海 总结 这份 攻略 赶紧 收藏 每次 惊喜 领略 王府 竹海 四季 春夏秋冬 这...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Topic  Count             Name  \\\n",
              "0     -1    427   -1_景区_城市_河南_游客   \n",
              "1      0    194  0_中国_石窟_龙门石窟_艺术   \n",
              "2      1    187    1_河南_活动_发展_中国   \n",
              "3      2    132    2_景区_游客_高速_旅客   \n",
              "4      3     60    3_景区_王府_竹海_旅行   \n",
              "\n",
              "                                  Representation  \\\n",
              "0     [景区, 城市, 河南, 游客, 洛阳市, 中国, 年票, 国家, 发展, 博物馆]   \n",
              "1  [中国, 石窟, 龙门石窟, 艺术, 河南, 世界, 历史, 博物馆, 文化遗产, 位于]   \n",
              "2     [河南, 活动, 发展, 中国, 洛阳市, 白云山, 主题, 服务, 历史, 工作]   \n",
              "3       [景区, 游客, 高速, 旅客, 郑州, 门票, 活动, 免费, 假期, 高铁]   \n",
              "4      [景区, 王府, 竹海, 旅行, 游客, 位于, 小浪底, 黄河, 仙门, 拍照]   \n",
              "\n",
              "                                 Representative_Docs  \n",
              "0  [小洛微 提醒 河南 多家 景区 陆续 发布 开园 公告 台风 杜苏芮 强度 逐渐 减弱 河...  \n",
              "1  [河南 旅游 之焦 洛阳 --- 龙门石窟 中国 石刻 艺术 宝库 现为 世界 文化遗产 全...  \n",
              "2  [行走 河南 读懂 中国 关注 全省 文旅文创 发展 大会 二十大 报告 指出 坚持 以文塑...  \n",
              "3  [日起 河南 景区 门票 河南省政府 新闻办 召开 新闻 发布会 获悉 豫见 春天 惠游 老...  \n",
              "4  [王府 竹海 总结 这份 攻略 赶紧 收藏 每次 惊喜 领略 王府 竹海 四季 春夏秋冬 这...  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 1. 词向量模型，同时加载本地训练好的词向量\n",
        "embedding_model = embedding_model = SentenceTransformer(\n",
        "  'paraphrase-multilingual-mpnet-base-v2',\n",
        ") # 使用Sentencetransformers模型\n",
        "embeddings = np.load('../../data/embedding_sen.npy') # 使用sentence-transformers向量\n",
        "print(embeddings.shape)\n",
        "\n",
        "# 2. 创建分词模型\n",
        "vectorizer_model = CountVectorizer() # 因为我们已经分好词了，所以这里不需要传入分词函数了\n",
        "\n",
        "# 3. 创建UMAP降维模型\n",
        "umap_model = UMAP(\n",
        "  n_neighbors=15,\n",
        "  n_components=5,\n",
        "  min_dist=0.0,\n",
        "  metric='cosine',\n",
        "  random_state=42  # ⚠️ 防止随机 https://maartengr.github.io/BERTopic/faq.html\n",
        ")\n",
        "\n",
        "# 4. 创建HDBSCAN聚类模型\n",
        "# 如果要建设离群值，可以减小下面两个参数\n",
        "# https://hdbscan.readthedocs.io/en/latest/faq.html\n",
        "hdbscan_model = HDBSCAN(\n",
        "  min_cluster_size=40,\n",
        "  min_samples=40,\n",
        ")\n",
        "\n",
        "# 5. 创建CountVectorizer模型\n",
        "vectorizer_model = CountVectorizer(stop_words=['洛阳', '旅游', '文化'])\n",
        "\n",
        "# 正式创建BERTopic模型\n",
        "topic_model = BERTopic(\n",
        "  embedding_model=embedding_model,\n",
        "  vectorizer_model=vectorizer_model,\n",
        "  umap_model=umap_model,\n",
        "  hdbscan_model=hdbscan_model,\n",
        ")\n",
        "\n",
        "# 查看主题\n",
        "topics, probs = topic_model.fit_transform(docs, embeddings=embeddings) #传入训练好的词向量\n",
        "topic_model.get_topic_info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
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                      "#2CA02C",
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                      "#9467BD",
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                      "#E377C2",
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                    "font": {
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                      "bgcolor": "white",
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                      "landcolor": "white",
                      "showlakes": true,
                      "showland": true,
                      "subunitcolor": "white"
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                    "hoverlabel": {
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                    "hovermode": "closest",
                    "mapbox": {
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                    "paper_bgcolor": "white",
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                        "showline": true,
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                        "showgrid": false,
                        "showline": true,
                        "ticks": "outside"
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                        "gridcolor": "rgb(232,232,232)",
                        "gridwidth": 2,
                        "linecolor": "rgb(36,36,36)",
                        "showbackground": true,
                        "showgrid": false,
                        "showline": true,
                        "ticks": "outside",
                        "zeroline": false,
                        "zerolinecolor": "rgb(36,36,36)"
                      },
                      "yaxis": {
                        "backgroundcolor": "white",
                        "gridcolor": "rgb(232,232,232)",
                        "gridwidth": 2,
                        "linecolor": "rgb(36,36,36)",
                        "showbackground": true,
                        "showgrid": false,
                        "showline": true,
                        "ticks": "outside",
                        "zeroline": false,
                        "zerolinecolor": "rgb(36,36,36)"
                      },
                      "zaxis": {
                        "backgroundcolor": "white",
                        "gridcolor": "rgb(232,232,232)",
                        "gridwidth": 2,
                        "linecolor": "rgb(36,36,36)",
                        "showbackground": true,
                        "showgrid": false,
                        "showline": true,
                        "ticks": "outside",
                        "zeroline": false,
                        "zerolinecolor": "rgb(36,36,36)"
                      }
                    },
                    "shapedefaults": {
                      "fillcolor": "black",
                      "line": {
                        "width": 0
                      },
                      "opacity": 0.3
                    },
                    "ternary": {
                      "aaxis": {
                        "gridcolor": "rgb(232,232,232)",
                        "linecolor": "rgb(36,36,36)",
                        "showgrid": false,
                        "showline": true,
                        "ticks": "outside"
                      },
                      "baxis": {
                        "gridcolor": "rgb(232,232,232)",
                        "linecolor": "rgb(36,36,36)",
                        "showgrid": false,
                        "showline": true,
                        "ticks": "outside"
                      },
                      "bgcolor": "white",
                      "caxis": {
                        "gridcolor": "rgb(232,232,232)",
                        "linecolor": "rgb(36,36,36)",
                        "showgrid": false,
                        "showline": true,
                        "ticks": "outside"
                      }
                    },
                    "title": {
                      "x": 0.05
                    },
                    "xaxis": {
                      "automargin": true,
                      "gridcolor": "rgb(232,232,232)",
                      "linecolor": "rgb(36,36,36)",
                      "showgrid": false,
                      "showline": true,
                      "ticks": "outside",
                      "title": {
                        "standoff": 15
                      },
                      "zeroline": false,
                      "zerolinecolor": "rgb(36,36,36)"
                    },
                    "yaxis": {
                      "automargin": true,
                      "gridcolor": "rgb(232,232,232)",
                      "linecolor": "rgb(36,36,36)",
                      "showgrid": false,
                      "showline": true,
                      "ticks": "outside",
                      "title": {
                        "standoff": 15
                      },
                      "zeroline": false,
                      "zerolinecolor": "rgb(36,36,36)"
                    }
                  }
                },
                "title": {
                  "font": {
                    "color": "Black",
                    "size": 22
                  },
                  "text": "<b>Documents and Topics</b>",
                  "x": 0.5,
                  "xanchor": "center",
                  "yanchor": "top"
                },
                "width": 1200,
                "xaxis": {
                  "visible": false
                },
                "yaxis": {
                  "visible": false
                }
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# UMAP可视化\n",
        "reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine',).fit_transform(embeddings)\n",
        "topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings, hide_document_hover=True, hide_annotations=False)"
      ]
    }
  ],
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